Traffic flow forecasting plays a crucial role and is the key technology to realize dynamic traffic guidance and active traffic control in intelligent traffic systems(ITS).Aiming at the complex local and global spatial...Traffic flow forecasting plays a crucial role and is the key technology to realize dynamic traffic guidance and active traffic control in intelligent traffic systems(ITS).Aiming at the complex local and global spatial-temporal dynamic characteristics of traffic flow,this paper proposes a new traffic flow forecasting model spatial-temporal attention graph neural network(STA-GNN)by combining at-tention mechanism(AM)and spatial-temporal convolutional network.The model learns the hidden dynamic local spatial correlations of the traffic network by combining the dynamic adjacency matrix constructed by the graph learning layer with the graph convolutional network(GCN).The local tem-poral correlations of traffic flow at different scales are extracted by stacking multiple convolutional kernels in temporal convolutional network(TCN).And the global spatial-temporal dependencies of long-time sequences of traffic flow are captured by the spatial-temporal attention mechanism(STAtt),which enhances the global spatial-temporal modeling and the representational ability of model.The experimental results on two datasets,METR-LA and PEMS-BAY,show the proposed STA-GNN model outperforms the common baseline models in forecasting accuracy.展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac...In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.展开更多
Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(...Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(MLT)and Abu Simbel(ABS)Egyptian geomagnetic observatories were processed and found to be of good quality.In this study,Egyptian observatory data were tested during both quiet and disturbed events and compared with data from INTERMAGNET observatories worldwide at different latitudes and within a narrow range of longitudes in both hemispheres.This study investigated the relationships between magnetic field components from Egyptian observatories and those from INTERMAGNET observatories using graphical representations of the X components;Pearson’s correlation for the X,Y,Z,and F components;cross-correlation for the X component;and wavelet coherence for the F component.The results of this study showed a high correlation between Egyptian observatories and all utilized INTERMAGNET stations,except those located at high latitudes,during both quiet and disturbed events.Additionally,the study confirmed the observed consistency between Egyptian observatories and selected INTERMAGNET stations.Therefore,Egyptian observatories can feasibly fill the gap in the Middle East and North Africa.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been pr...Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been proposed.However,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small datasets.Still,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective features.To address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME Recognition.The proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective features.The outputs are then fused,concatenated,and classified using the XgBoost algorithm.This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of XgBoost.The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent models.The accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,respectively.The proposed method has the advantage of operational efficiency and less computational time.展开更多
Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton s...Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.展开更多
The hazard produced by natural phenomena on infrastructure and urban populations has been widely studied in the last 50 years. Researchers have recognised that the real danger posed by these phenomena depends on their...The hazard produced by natural phenomena on infrastructure and urban populations has been widely studied in the last 50 years. Researchers have recognised that the real danger posed by these phenomena depends on their extreme values. Most researchers focus on the extremes of natural phenomena considered in isolation, one variable at a time. However, what is relevant in hazard studies is coincident extremes of several climatic variables, i.e., the presence of compound extremes. The peak value of these extremes seldom coincides, but off-peak values located in the tail of the distributions are often concurrent and can lead to catastrophic events. What is essential in hazard studies is to calculate the probabilistic distribution of the extremes of coincident climatic variables. The presence of correlations between these variables complicates the problem. This paper presents a computationally efficient and robust mathematical methodology to solve the problem. The procedure is based on the convolution of the distributions of the climatic variables. Once the probabilistic distribution of the compound variables is found, it is possible to calculate the curves of the return period, which is the indicator of importance in hazard and risk studies. This compound Return Period is computed using the Statistics of Extreme Values. To illustrate the problem, the case of a cyclone landing close to a low-gradient coastal city is discussed, and its probability of flooding and recurrence period is calculated. We show that the failure to correctly model the correlation between variables can result in overestimating the Return Period curve, consequently increasing mitigation costs.展开更多
Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign cur...Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.展开更多
Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the m...Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.展开更多
Background: The use of assisted reproductive technique (ART) is becoming more common in infertility. During ART most patients undergo ovarian stimulation. In this study we study the correlation between ovarian reserve...Background: The use of assisted reproductive technique (ART) is becoming more common in infertility. During ART most patients undergo ovarian stimulation. In this study we study the correlation between ovarian reserve markers: Anti-Mullerian hormone (AMH) and antral follicle count (AFC), and the response to ovarian stimulation at in vitro fertilization (IVF) centres in Douala Cameroon. Methods: This was a hospital based cross-sectional sectional analytic study carried out over a period of 3 years, 4 months at Clinique de l’Aéroport, Clinique Odyssée and Clinique Urogyn. Inclusion criteria were: Female partners of infertile couples undergoing ovarian stimulation for an in vitro fertilization cycle, patients who had both ovaries and had done either AMH, AFC or both before ovarian stimulation. Patients were divided into three groups based on the number of oocytes retrieved: low ovarian response for ≤3 oocytes, normal ovarian response for 4 - 15 oocytes and high ovarian response for >15 oocytes. Data obtained was analyzed by SPSS version 25.0. Results: The ages of participants ranged from 20 - 4 7 years, with a mean age of 34.11 ± 5.11 years. Most of them had secondary infertility (57.9%). The GnRH antagonist protocol was mainly used, and ovulation was triggered using HCG predominantly. On Multivariate analysis, age and history of PCOS were significantly associated with ovarian response in the low and high ovarian response groups, respectively. Conclusion: AMH has a better predictive value than AFC, however, it is less sensitive but more specific than AFC.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
BACKGROUND Previous cellular studies have demonstrated that elevated expression of Cx43 promotes the degradation of cyclin E1 and inhibits cell proliferation through ubiquitination.Conversely,reduced expression result...BACKGROUND Previous cellular studies have demonstrated that elevated expression of Cx43 promotes the degradation of cyclin E1 and inhibits cell proliferation through ubiquitination.Conversely,reduced expression results in a loss of this capacity to facilitate cyclin E degradation.The ubiquitination and degradation of cyclin E1 may be associated with phosphorylation at specific sites on the protein,with Cx43 potentially enhancing this process by facilitating the phosphorylation of these critical residues.AIM To investigate the correlation between expression of Cx43,SKP1/Cullin1/F-box(SCF)FBXW7,p-cyclin E1(ser73,thr77,thr395)and clinicopathological indexes in colon cancer.METHODS Expression levels of Cx43,SCF^(FBXW7),p-cyclin E1(ser73,thr77,thr395)in 38 clinical colon cancer samples were detected by immunohistochemistry and were analyzed by statistical methods to discuss their correlations.RESULTS Positive rate of Cx43,SCF^(FBXW7),p-cyclin E1(Ser73),p-cyclin E1(Thr77)and p-cyclin E1(Thr395)in detected samples were 76.32%,76.32%,65.79%,5.26%and 55.26%respectively.Positive expressions of these proteins were not related to the tissue type,degree of tissue differentiation or lymph node metastasis.Cx43 and SCF^(FBXW7)(r=0.749),p-cyclin E1(Ser73)(r=0.667)and p-cyclin E1(Thr395)(r=0.457),SCF^(FBXW7) and p-cyclin E1(Ser73)(r=0.703)and p-cyclin E1(Thr395)(0.415)were correlated in colon cancer(P<0.05),and expressions of the above proteins were positively correlated in colon cancer.CONCLUSION Cx43 may facilitate the phosphorylation of cyclin E1 at the Ser73 and Thr195 sites through its interaction with SCF^(FBXW7),thereby influencing the ubiquitination and degradation of cyclin E1.展开更多
Bone marrow edema syndrome (BMES), is a rare and self-limiting condition characterized by localized bone pain and transient marrow edema visible on MRI. BMES has been increasingly associated with specific cutaneous ma...Bone marrow edema syndrome (BMES), is a rare and self-limiting condition characterized by localized bone pain and transient marrow edema visible on MRI. BMES has been increasingly associated with specific cutaneous manifestations that may hold diagnostic and prognostic significance. Patients with BMES have reported localized erythema, dermal thickening, and induration overlying the affected joints, which are hypothesized to reflect microvascular compromise and inflammatory processes within the bone and adjacent soft tissues. Dermatologic signs are likely linked to regional hyperemia, venous stasis, and cytokine-mediated inflammation, paralleling the pathophysiological mechanisms underlying intraosseous edema. Elevated intraosseous pressure in BMES may disrupt local perfusion, resulting in ischemia-reperfusion injury and subsequent vascular leakage, which manifests in visible cutaneous changes. Pro-inflammatory mediators, such as interleukin-1β and vascular endothelial growth factor (VEGF), central to BMES pathogenesis, may exacerbate endothelial activation, and dermal involvement. Histopathologic studies of affected skin have revealed perivascular lymphocytic infiltration and increased dermal vascularity, further supporting the theory of a shared ischemic and inflammatory pathway between bone and skin. Although MRI remains the gold standard for BMES diagnosis, recognition of these cutaneous manifestations could expedite orthopedic referral and intervention, especially in cases where imaging is delayed or symptoms are ambiguous. Current treatment options, including bisphosphonates, prostacyclin analogs, and offloading of weight bearing, may benefit from integration with dermatologic strategies to alleviate localized cutaneous symptoms and improve patient comfort. Evaluating the molecular and vascular links between BMES and its cutaneous manifestations provides an opportunity to refine diagnostic protocols and therapeutic approaches, offering a comprehensive understanding of the systemic interplay between dermal and skeletal pathophysiology, and optimizing clinical outcomes for patients affected by BMES.展开更多
The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has signifi...The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.展开更多
The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation method...The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation methods specifically include sample surveys and harvesting statistics.Although these methods have high estimation accuracy,they are time-consuming,destructive,and difficult to implement to monitor the biomass at a large scale.The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGBbased on improved convolutional features(CFs).Low-cost unmanned aerial vehicles(UAV)were used as the main data acquisition equipment.This study acquired image data acquired by RGB camera(RGB)and multi-spectral(MS)image data of the wheat population canopy for two wheat varieties and five key growth stages.Then,field measurements were conducted to obtain the actual wheat biomass data for validation.Based on the remote sensing indices(RSIs),structural features(SFs),and CFs,this study proposed a new feature named AUR-50(multi-source combination based on convolutional feature optimization)to estimate the wheat AGB.The results show that AUR-50 could estimate the wheat AGB more accurately than RSIs and SFs,and the average R^(2) exceeded 0.77.In the overwintering period,AUR-50_(MS)(multi-source combination with convolutional feature optimization using multispectral imagery)had the highest estimation accuracy(R^(2) of 0.88).In addition,AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs,where the highest R^(2) was 0.69 at the flowering stage.The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.展开更多
Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,...Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.展开更多
To study the main aroma components of Phalaenopsis orchid and their relationship with colors,10 fragrant cultivars with different colors,like pink,rose,yellow,and purple,were used as samples in this experiment.Headspa...To study the main aroma components of Phalaenopsis orchid and their relationship with colors,10 fragrant cultivars with different colors,like pink,rose,yellow,and purple,were used as samples in this experiment.Headspace-gas chromatography-mass spectrometry was used to determine the main components of floral fragrance and analyze the correlation between floral color and fragrance.The results showed that the main aroma components of the 10 fragrant cultivars of Phalaenopsis were alcohols,alkenes,esters,and benzene ring compounds,and the main aroma components of different cultivars were diverse.The main aroma components of yellow fragrant flowers were esters,alcohols,and alkenes.The purple and pink series were alcohols and phenyl rings.There was a certain correlation between flower color and floral fragrance.There was a significant positive correlation between esters and flower color C^(*)value,and a significant negative correlation between alkenes and flower color h value.There was a significant negative correlation between alcohol and flower color C^(*)value,and a significant positive correlation between alcohol and L^(*)value.The content of benzene compounds was negatively correlated with L^(*)and positively correlated with h value.This may be related to the synthesizing of anthocyanins and benzene ring compounds through the phenylpropanoid metabolic pathway.In this paper,the correlation between Phalaenopsis floral color and fragrance was studied,and the synthetic pathway of floral color and fragrance components was analyzed.The proposed method and research data can provide a theoretical basis for floral color breeding and fragrance synthesis.展开更多
In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image blurring.Conventional deblurring m...In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image blurring.Conventional deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational resources.To address these issues,this study proposes a deep learning-based approach tailored for PSP image deblurring.Considering that PSP applications primarily involve the accurate pressure measurements of complex geometries,the images captured under such conditions exhibit distinctive non-uniform motion blur,presenting challenges for standard deep learning models utilizing convolutional or attention-based techniques.In this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance performance.Furthermore,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow features.To preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image reconstruction.By integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data analysis.Experimental evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.展开更多
Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence.Due to the advantages in computing speed,integrated photonic chips...Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence.Due to the advantages in computing speed,integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm.Programmable photonic chips are vital for achieving practical applications of photonic computing.Herein,a programmable photonic chip based on ultrafast laser-induced phase change is fabricated for photonic computing.Through designing the ultrafast laser pulses,the Sb film integrated into photonic waveguides can be reversibly switched between crystalline and amorphous phase,resulting in a large contrast in refractive index and extinction coefficient.As a consequence,the light transmission of waveguides can be switched between write and erase states.To determine the phase change time,the transient laser-induced phase change dynamics of Sb film are revealed at atomic scale,and the time-resolved transient reflectivity is measured.Based on the integrated photonic chip,photonic convolutional neural networks are built to implement machine learning algorithm,and images recognition task is achieved.This work paves a route for fabricating programmable photonic chips by designed ultrafast laser,which will facilitate the application of photonic computing in artificial intelligence.展开更多
基金Supported by the Key R&D Program of Gansu Province(No.23YFGA0063)the National Natural Science Foundation of China(No.62363022,61663021)+1 种基金the Natural Science Foundation of Gansu Province(No.22JR5RA226,23JRRA886)the Gansu Provincial De-partment of Education:Industrial Support Plan Project(No.2023CYZC-35).
文摘Traffic flow forecasting plays a crucial role and is the key technology to realize dynamic traffic guidance and active traffic control in intelligent traffic systems(ITS).Aiming at the complex local and global spatial-temporal dynamic characteristics of traffic flow,this paper proposes a new traffic flow forecasting model spatial-temporal attention graph neural network(STA-GNN)by combining at-tention mechanism(AM)and spatial-temporal convolutional network.The model learns the hidden dynamic local spatial correlations of the traffic network by combining the dynamic adjacency matrix constructed by the graph learning layer with the graph convolutional network(GCN).The local tem-poral correlations of traffic flow at different scales are extracted by stacking multiple convolutional kernels in temporal convolutional network(TCN).And the global spatial-temporal dependencies of long-time sequences of traffic flow are captured by the spatial-temporal attention mechanism(STAtt),which enhances the global spatial-temporal modeling and the representational ability of model.The experimental results on two datasets,METR-LA and PEMS-BAY,show the proposed STA-GNN model outperforms the common baseline models in forecasting accuracy.
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Natural Science Foundation of China(62272049,62236006,62172045)the Key Projects of Beijing Union University(ZKZD202301).
文摘In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.
文摘Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(MLT)and Abu Simbel(ABS)Egyptian geomagnetic observatories were processed and found to be of good quality.In this study,Egyptian observatory data were tested during both quiet and disturbed events and compared with data from INTERMAGNET observatories worldwide at different latitudes and within a narrow range of longitudes in both hemispheres.This study investigated the relationships between magnetic field components from Egyptian observatories and those from INTERMAGNET observatories using graphical representations of the X components;Pearson’s correlation for the X,Y,Z,and F components;cross-correlation for the X component;and wavelet coherence for the F component.The results of this study showed a high correlation between Egyptian observatories and all utilized INTERMAGNET stations,except those located at high latitudes,during both quiet and disturbed events.Additionally,the study confirmed the observed consistency between Egyptian observatories and selected INTERMAGNET stations.Therefore,Egyptian observatories can feasibly fill the gap in the Middle East and North Africa.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金supported by the Key Research and Development Program of Jiangsu Province under Grant BE2022059-3,CTBC Bank through the Industry-Academia Cooperation Project,as well as by the Ministry of Science and Technology of Taiwan through Grants MOST-108-2218-E-002-055,MOST-109-2223-E-009-002-MY3,MOST-109-2218-E-009-025,and MOST431109-2218-E-002-015.
文摘Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been proposed.However,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small datasets.Still,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective features.To address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME Recognition.The proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective features.The outputs are then fused,concatenated,and classified using the XgBoost algorithm.This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of XgBoost.The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent models.The accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,respectively.The proposed method has the advantage of operational efficiency and less computational time.
基金supported in part by the National Natural Science Foundation of China under Grants 61973065,U20A20197,61973063.
文摘Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.
文摘The hazard produced by natural phenomena on infrastructure and urban populations has been widely studied in the last 50 years. Researchers have recognised that the real danger posed by these phenomena depends on their extreme values. Most researchers focus on the extremes of natural phenomena considered in isolation, one variable at a time. However, what is relevant in hazard studies is coincident extremes of several climatic variables, i.e., the presence of compound extremes. The peak value of these extremes seldom coincides, but off-peak values located in the tail of the distributions are often concurrent and can lead to catastrophic events. What is essential in hazard studies is to calculate the probabilistic distribution of the extremes of coincident climatic variables. The presence of correlations between these variables complicates the problem. This paper presents a computationally efficient and robust mathematical methodology to solve the problem. The procedure is based on the convolution of the distributions of the climatic variables. Once the probabilistic distribution of the compound variables is found, it is possible to calculate the curves of the return period, which is the indicator of importance in hazard and risk studies. This compound Return Period is computed using the Statistics of Extreme Values. To illustrate the problem, the case of a cyclone landing close to a low-gradient coastal city is discussed, and its probability of flooding and recurrence period is calculated. We show that the failure to correctly model the correlation between variables can result in overestimating the Return Period curve, consequently increasing mitigation costs.
文摘Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.
文摘Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.
文摘Background: The use of assisted reproductive technique (ART) is becoming more common in infertility. During ART most patients undergo ovarian stimulation. In this study we study the correlation between ovarian reserve markers: Anti-Mullerian hormone (AMH) and antral follicle count (AFC), and the response to ovarian stimulation at in vitro fertilization (IVF) centres in Douala Cameroon. Methods: This was a hospital based cross-sectional sectional analytic study carried out over a period of 3 years, 4 months at Clinique de l’Aéroport, Clinique Odyssée and Clinique Urogyn. Inclusion criteria were: Female partners of infertile couples undergoing ovarian stimulation for an in vitro fertilization cycle, patients who had both ovaries and had done either AMH, AFC or both before ovarian stimulation. Patients were divided into three groups based on the number of oocytes retrieved: low ovarian response for ≤3 oocytes, normal ovarian response for 4 - 15 oocytes and high ovarian response for >15 oocytes. Data obtained was analyzed by SPSS version 25.0. Results: The ages of participants ranged from 20 - 4 7 years, with a mean age of 34.11 ± 5.11 years. Most of them had secondary infertility (57.9%). The GnRH antagonist protocol was mainly used, and ovulation was triggered using HCG predominantly. On Multivariate analysis, age and history of PCOS were significantly associated with ovarian response in the low and high ovarian response groups, respectively. Conclusion: AMH has a better predictive value than AFC, however, it is less sensitive but more specific than AFC.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金Supported by Innovative Practice Platform for Undergraduate Students,School of Public Health Xiamen University,No.2021001.
文摘BACKGROUND Previous cellular studies have demonstrated that elevated expression of Cx43 promotes the degradation of cyclin E1 and inhibits cell proliferation through ubiquitination.Conversely,reduced expression results in a loss of this capacity to facilitate cyclin E degradation.The ubiquitination and degradation of cyclin E1 may be associated with phosphorylation at specific sites on the protein,with Cx43 potentially enhancing this process by facilitating the phosphorylation of these critical residues.AIM To investigate the correlation between expression of Cx43,SKP1/Cullin1/F-box(SCF)FBXW7,p-cyclin E1(ser73,thr77,thr395)and clinicopathological indexes in colon cancer.METHODS Expression levels of Cx43,SCF^(FBXW7),p-cyclin E1(ser73,thr77,thr395)in 38 clinical colon cancer samples were detected by immunohistochemistry and were analyzed by statistical methods to discuss their correlations.RESULTS Positive rate of Cx43,SCF^(FBXW7),p-cyclin E1(Ser73),p-cyclin E1(Thr77)and p-cyclin E1(Thr395)in detected samples were 76.32%,76.32%,65.79%,5.26%and 55.26%respectively.Positive expressions of these proteins were not related to the tissue type,degree of tissue differentiation or lymph node metastasis.Cx43 and SCF^(FBXW7)(r=0.749),p-cyclin E1(Ser73)(r=0.667)and p-cyclin E1(Thr395)(r=0.457),SCF^(FBXW7) and p-cyclin E1(Ser73)(r=0.703)and p-cyclin E1(Thr395)(0.415)were correlated in colon cancer(P<0.05),and expressions of the above proteins were positively correlated in colon cancer.CONCLUSION Cx43 may facilitate the phosphorylation of cyclin E1 at the Ser73 and Thr195 sites through its interaction with SCF^(FBXW7),thereby influencing the ubiquitination and degradation of cyclin E1.
文摘Bone marrow edema syndrome (BMES), is a rare and self-limiting condition characterized by localized bone pain and transient marrow edema visible on MRI. BMES has been increasingly associated with specific cutaneous manifestations that may hold diagnostic and prognostic significance. Patients with BMES have reported localized erythema, dermal thickening, and induration overlying the affected joints, which are hypothesized to reflect microvascular compromise and inflammatory processes within the bone and adjacent soft tissues. Dermatologic signs are likely linked to regional hyperemia, venous stasis, and cytokine-mediated inflammation, paralleling the pathophysiological mechanisms underlying intraosseous edema. Elevated intraosseous pressure in BMES may disrupt local perfusion, resulting in ischemia-reperfusion injury and subsequent vascular leakage, which manifests in visible cutaneous changes. Pro-inflammatory mediators, such as interleukin-1β and vascular endothelial growth factor (VEGF), central to BMES pathogenesis, may exacerbate endothelial activation, and dermal involvement. Histopathologic studies of affected skin have revealed perivascular lymphocytic infiltration and increased dermal vascularity, further supporting the theory of a shared ischemic and inflammatory pathway between bone and skin. Although MRI remains the gold standard for BMES diagnosis, recognition of these cutaneous manifestations could expedite orthopedic referral and intervention, especially in cases where imaging is delayed or symptoms are ambiguous. Current treatment options, including bisphosphonates, prostacyclin analogs, and offloading of weight bearing, may benefit from integration with dermatologic strategies to alleviate localized cutaneous symptoms and improve patient comfort. Evaluating the molecular and vascular links between BMES and its cutaneous manifestations provides an opportunity to refine diagnostic protocols and therapeutic approaches, offering a comprehensive understanding of the systemic interplay between dermal and skeletal pathophysiology, and optimizing clinical outcomes for patients affected by BMES.
基金Saudi Arabia for funding this work through Small Research Group Project under Grant Number RGP.1/316/45.
文摘The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1973)the National Natural Science Foundation of China(32172110,32071945)+7 种基金the Key Research and Development Program(Modern Agriculture)of Jiangsu Province,China(BE2022342-2,BE2020319)the Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center Open Project,China(ZHKF04)the National Key Research and Development Program of China(2023YFD2300201,2023YFD1202200)the Special Funds for Scientific and Technological Innovation of Jiangsu Province,China(BE2022425)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD)the Central Publicinterest Scientific Institution Basal Research Fund,China(JBYW-AII-2023-08)the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences(CAAS-CS-202201)the Special Fund for Independent Innovation of Agriculture Science and Technology in Jiangsu Province,China(CX(22)3112)。
文摘The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation methods specifically include sample surveys and harvesting statistics.Although these methods have high estimation accuracy,they are time-consuming,destructive,and difficult to implement to monitor the biomass at a large scale.The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGBbased on improved convolutional features(CFs).Low-cost unmanned aerial vehicles(UAV)were used as the main data acquisition equipment.This study acquired image data acquired by RGB camera(RGB)and multi-spectral(MS)image data of the wheat population canopy for two wheat varieties and five key growth stages.Then,field measurements were conducted to obtain the actual wheat biomass data for validation.Based on the remote sensing indices(RSIs),structural features(SFs),and CFs,this study proposed a new feature named AUR-50(multi-source combination based on convolutional feature optimization)to estimate the wheat AGB.The results show that AUR-50 could estimate the wheat AGB more accurately than RSIs and SFs,and the average R^(2) exceeded 0.77.In the overwintering period,AUR-50_(MS)(multi-source combination with convolutional feature optimization using multispectral imagery)had the highest estimation accuracy(R^(2) of 0.88).In addition,AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs,where the highest R^(2) was 0.69 at the flowering stage.The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.
基金supported by Science and Technology Research Youth Project of Chongqing Municipal Education Commission(No.KJQN202301104)Cooperative Project between universities in Chongqing and Affiliated Institutes of Chinese Academy of Sciences(No.HZ2021011)+1 种基金Chongqing Municipal Science and Technology Commission Technology Innovation and Application Development Special Project(No.2022TIAD-KPX0040)Action Plan for Quality Development of Chongqing University of Technology Graduate Education(Grant No.gzlcx20242014).
文摘Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.
基金supported by the Shandong Province Key Research and Development Plan Project(ID Numbers 2024LZGC026 and 2021LZGC019)Shanghai Science and Technology Agriculture Project(ID Number 2020-02-08-00-12-F01463).
文摘To study the main aroma components of Phalaenopsis orchid and their relationship with colors,10 fragrant cultivars with different colors,like pink,rose,yellow,and purple,were used as samples in this experiment.Headspace-gas chromatography-mass spectrometry was used to determine the main components of floral fragrance and analyze the correlation between floral color and fragrance.The results showed that the main aroma components of the 10 fragrant cultivars of Phalaenopsis were alcohols,alkenes,esters,and benzene ring compounds,and the main aroma components of different cultivars were diverse.The main aroma components of yellow fragrant flowers were esters,alcohols,and alkenes.The purple and pink series were alcohols and phenyl rings.There was a certain correlation between flower color and floral fragrance.There was a significant positive correlation between esters and flower color C^(*)value,and a significant negative correlation between alkenes and flower color h value.There was a significant negative correlation between alcohol and flower color C^(*)value,and a significant positive correlation between alcohol and L^(*)value.The content of benzene compounds was negatively correlated with L^(*)and positively correlated with h value.This may be related to the synthesizing of anthocyanins and benzene ring compounds through the phenylpropanoid metabolic pathway.In this paper,the correlation between Phalaenopsis floral color and fragrance was studied,and the synthetic pathway of floral color and fragrance components was analyzed.The proposed method and research data can provide a theoretical basis for floral color breeding and fragrance synthesis.
基金supported by the National Natural Science Foundation of China(No.12202476).
文摘In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image blurring.Conventional deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational resources.To address these issues,this study proposes a deep learning-based approach tailored for PSP image deblurring.Considering that PSP applications primarily involve the accurate pressure measurements of complex geometries,the images captured under such conditions exhibit distinctive non-uniform motion blur,presenting challenges for standard deep learning models utilizing convolutional or attention-based techniques.In this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance performance.Furthermore,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow features.To preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image reconstruction.By integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data analysis.Experimental evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.
基金supported by the National Key R&D Program of China(2024YFB4609801)the National Natural Science Foundation of China(52075289)the Tsinghua-Jiangyin Innovation Special Fund(TJISF,2023JYTH0104).
文摘Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence.Due to the advantages in computing speed,integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm.Programmable photonic chips are vital for achieving practical applications of photonic computing.Herein,a programmable photonic chip based on ultrafast laser-induced phase change is fabricated for photonic computing.Through designing the ultrafast laser pulses,the Sb film integrated into photonic waveguides can be reversibly switched between crystalline and amorphous phase,resulting in a large contrast in refractive index and extinction coefficient.As a consequence,the light transmission of waveguides can be switched between write and erase states.To determine the phase change time,the transient laser-induced phase change dynamics of Sb film are revealed at atomic scale,and the time-resolved transient reflectivity is measured.Based on the integrated photonic chip,photonic convolutional neural networks are built to implement machine learning algorithm,and images recognition task is achieved.This work paves a route for fabricating programmable photonic chips by designed ultrafast laser,which will facilitate the application of photonic computing in artificial intelligence.
